Tensorflow API | Tensorflow Asp.Net

Here I extend the API to train on a new object that is not part of the COCO dataset. In this case I chose a toy that was lying around. See gif below. So far, I have been impressed by the performance of the API. The steps highlighted here can be extended to any single or multiple object detector that you want to build. Tensorflow Toy Detector~ You can find the code on my  Github  repo Collecting data The first step is collecting images for your project. You could download them from google ensuring you have a wide variation in angles, brightness, scale etc. In my case I created a video of the little aeroplane toy and used  Opencv  to extract images from the video. This saved me a lot of time. I ensured that images were taken from multiple angles. You can also randomly change brightness for some of the images so that the detector can work under different conditions of lightning. Overall 100–150 pics will suffice. See some sample images below: ...

In this post I want to explore some of the key similarities and differences between two popular deep learning frameworks: PyTorch and TensorFlow. Why those two and not the others? There are many deep learning frameworks and many of them are viable tools, I chose those two just because I was interested in comparing them specifically. Origins TensorFlow is developed by Google Brain and actively used at Google both for research and production needs. Its closed-source predecessor is called DistBelief. PyTorch is a cousin of lua-based Torch framework which is actively used at Facebook. However, PyTorch is not a simple set of wrappers to support popular language, it was rewritten and tailored to be fast and feel native. The best way to compare two frameworks is to code something up in both of them. I’ve written a companion jupyter notebook for this post and you can  get it here . All code will be provided in the post too. First, let’s code a simple approximator for t...